Positioning method and apparatus

ABSTRACT

The present application discloses a positioning method and an apparatus, which relate to the technical field of intelligent driving. A specific implementation solution is: determining a first positioning result using point cloud data collected by lidar in combination with a laser point cloud reflection value map; constructing a constraint condition using the first positioning result, where the constraint condition is used to accelerate a convergence speed of solving a receiver position using observation data; performing GNSS-PPP positioning using the constraint condition in combination with observation data of a GNSS receiver to obtain a second positioning result. Using this solution, lidar positioning technology is combined with GNSS-PPP positioning technology to realize a purpose of not relying on a GNSS base station.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.202010401600.4, filed on May 13, 2020, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

Embodiments of the present application relate to the technical field ofautomatic driving and, in particular, to a positioning method and anapparatus.

BACKGROUND

At present, a positioning system plays an important role in a process ofautomatic driving and assisted driving of an unmanned vehicle, and othermodules, such as perception and path planning, perform correspondingoperations based on a positioning result generated by the positioningsystem to varying degrees. The accuracy of positioning becomes one ofkey factors affecting success or failure of automatic driving orassisted driving.

The global navigation satellite system (Global Navigation SatelliteSystem, GNSS) is a common positioning system, and positioningtechnologies based on GNSS mainly include real-time kinematic (Real-timekinematic, RTK) technology and precise point positioning (precise pointpositioning, PPP) technology. In RTK technology, differential RTKpositioning is carried out between a GNSS base station and a vehicle torealize positioning with centimeter-level precision for the vehicle, byutilizing characteristics of close distance between the GNSS basestation and the vehicle and relatively strong correlation of a GNSSobservation error. In order to get an accurate positioning resultquickly, a large number of GNSS base stations need to be deployed. InPPP technology, a satellite orbit with centimeter-level high-precision,a clock difference and a GNSS observation value at a vehicle end areused to realize positioning with centimeter-level precision for thevehicle. However, because there are many parameters to be estimated andsatellite geometry changes slowly, it takes about half an hour toconverge.

Obviously, how to provide a positioning method that does not rely on aGNSS base station and has fast convergence speed is regarded as anurgent problem to be solved in the industry.

SUMMARY

Embodiments of the present application provide a positioning method andan apparatus. A convergence speed of PPP positioning is accelerated byfusing lidar positioning, and a purpose of fast and accurate positioningwithout relying on a GNSS base station is realized.

In a first aspect, an embodiment of the present application provides apositioning method, including: determining a first positioning resultusing point cloud data of a vehicle at a current positioning time, wherethe first positioning result is used to indicate a position of thevehicle in a pre-constructed laser point cloud reflection value map atthe current positioning time; constructing a constraint condition usingthe first positioning result, where the constraint condition is used toindicate a position relationship between a position of a vehicle-mountedGNSS receiver determined according to the first positioning result and asecond positioning result, and the second positioning result is used toindicate a position of the vehicle in a rectangular coordinate system atthe current positioning time; performing precise point positioning PPPusing the constraint condition in combination with observation data ofthe vehicle-mounted GNSS receiver to obtain the second positioningresult; controlling the vehicle using the second positioning result.Using this solution, lidar positioning technology is combined withGNSS-PPP positioning technology to realize a purpose of not relying on aGNSS base station. Meanwhile, using a positioning result of the lidarpositioning technology, the constraint condition that can accelerate aconvergence speed of solving the position of the receiver using theobservation data can be constructed, thereby avoiding a drawback of theconvergence speed being too slow. Therefore, the convergence speed ofGNSS-PPP positioning is accelerated by fusing lidar positioning, and apurpose of fast and accurate positioning without relying on the GNSSbase station is realized.

In a feasible design, the constructing the constraint condition usingthe first positioning result includes: determining a position of theGNSS receiver in the rectangular coordinate system according to thefirst positioning result; constructing the constraint conditionaccording to the position of the GNSS receiver in the rectangularcoordinate system, where the constraint condition satisfies thefollowing equations:

(X_(Lidar_fix) − X)² + (Y_(Lidar_fix) − Y)² + (Z_(Lidar_fix) − Z)² = 0$\frac{\partial X_{{Lidar}\_{fix}}}{\partial X} = 1$$\frac{\partial Y_{{Lidar}\_{fix}}}{\partial Y} = 1$${\frac{\partial Z_{{Lidar}\_{fix}}}{\partial Z} = 1};$

where X_(Lidar_fix) represents x-coordinate of the vehicle-mounted GNSSreceiver in the rectangular coordinate system, Y_(Lidar_fix) representsy-coordinate of the vehicle-mounted GNSS receiver in the rectangularcoordinate system, Z_(Lidar_fix) represents z-coordinate of thevehicle-mounted GNSS receiver in the rectangular coordinate system, Xrepresents x-coordinate of the second positioning result to be solved, Yrepresents y-coordinate of the second positioning result to be solved,and Z represents z-coordinate of the second positioning result to besolved. Using this solution, the convergence speed of the GNSS-PPPpositioning is accelerated by fusing the lidar positioning, and thepurpose of fast and accurate positioning without relying on the GNSSbase station is realized.

In a feasible design, the performing the precise point positioning PPPusing the constraint condition in combination with the observation dataof the vehicle-mounted GNSS receiver to obtain the second positioningresult includes: constructing observation equations using theobservation data; performing PPP positioning using the observationequations in combination with the constraint condition to obtain thesecond positioning result, where the observation equations are asfollows:

λφ=r+l·dx+dt·C+T+I+ε+λ·N

ρ=r+l·dx+dt·C+T−I+ε

where ρ represents a pseudo-range between a satellite and the vehicle, Nrepresents a carrier phase integer ambiguity, r represents a distancebetween the satellite and the GNSS receiver, l represents cosine of anobservation direction, dt represents a correction amount of a clockdifference between the satellite and the vehicle-mounted GNSS receiver,T represents a tropospheric deviation, I represents an ionosphericdeviation, ε represents a noise constant, dx represents a state variableto be estimated, including a coordinate increment and a clock differencechange; and C represents a speed of light in a vacuum. Using thissolution, the convergence speed of the GNSS-PPP positioning isaccelerated by fusing the lidar positioning, and the purpose of fast andaccurate positioning without relying on the GNSS base station isrealized.

In a feasible design, the determining the first positioning result usingthe point cloud data of the vehicle at the current positioning timeincludes: converting the point cloud data from a vehicle body coordinatesystem to a world coordinate system to obtain converted data; projectingthe converted data to the laser point cloud reflection value map toobtain a projection area; determining a plurality of to-be-matched areasfrom the laser point cloud reflection value map according to theprojection area; determining a matching probability of the projectionarea and each of the to-be-matched areas to obtain a plurality ofmatching probabilities; determining the first positioning resultaccording to the plurality of matching probabilities. Using thissolution, a purpose of determining the first positioning result usingthe point cloud data is realized.

In a feasible design, the determining the first positioning resultaccording to the plurality of matching probabilities includes:determining a prediction probability of each of the plurality ofmatching probabilities to obtain a plurality of predictionprobabilities, where each of the prediction probabilities is a matchingprobability corresponding to a first positioning time before the currentpositioning time; updating each matching probability of the plurality ofmatching probabilities using respective prediction probabilitiescorresponding to the plurality of matching probabilities to obtain aplurality of updated matching probabilities; determining a maximummatching probability from the plurality of updated matchingprobabilities; determining the first positioning result using ato-be-matched area corresponding to the maximum matching probability.Using this solution, accuracy of the first positioning result isimproved.

In a feasible design, the updating each matching probability of theplurality of matching probabilities using respective predictionprobabilities corresponding to the plurality of matching probabilitiesto obtain the plurality of updated matching probabilities includes: foreach matching probability, determining an updated matching probabilityusing a product of a preset normalization coefficient, the matchingprobability and the prediction probability corresponding to the matchingprobability. Using this solution, a purpose of improving the accuracy ofthe first positioning result is realized.

In a feasible design, before performing the precise point positioningPPP using the constraint condition in combination with the observationdata of the vehicle-mounted GNSS receiver to obtain the secondpositioning result, the method further includes: constructingpseudo-range and carrier single difference observation equations;filtering the observation data using the pseudo-range and carrier singledifference observation equations to filter out error data in theobservation data. Using this solution, a purpose of improving accuracyof the second positioning result is realized by filtering out data witha larger error.

In a feasible design, before determining the first positioning resultusing the point cloud data of the vehicle at the current positioningtime, the method further includes: dividing a ground plane of an earthsurface in the world coordinate system into a plurality of map_nodeswith a same size and shape; dividing each map_node of the plurality ofmap_nodes into a plurality of map_cells with a same size and shape; andstoring corresponding map data in each map_cell of the plurality ofmap_cells. Using this solution, a purpose of constructing a laserreflection value map offline is realized.

In a feasible design, the map data includes at least one of thefollowing data: a mean value of laser reflection intensity values oflaser points within a positioning position corresponding to themap_cell, a variance of the laser reflection intensity values of thelaser points within the positioning position corresponding to themap_cell, and a quantity of the laser points within the positioningposition corresponding to the map_cell.

In a second aspect, an embodiment of the present application provides apositioning apparatus, including:

a determining module, configured to determine a first positioning resultusing point cloud data of a vehicle at a current positioning time, wherethe first positioning result is used to indicate a position of thevehicle in a pre-constructed laser point cloud reflection value map atthe current positioning time;

a first constructing module, configured to construct a constraintcondition using the first positioning result, where the constraintcondition is used to indicate a position relationship between a positionof a vehicle-mounted GNSS receiver determined according to the firstpositioning result and a second positioning result, and the secondpositioning result is used to indicate a position of the vehicle in arectangular coordinate system at the current positioning time;

a positioning module, configured to perform precise point positioningPPP using the constraint condition in combination with observation dataof the vehicle-mounted GNSS receiver to obtain the second positioningresult;

a controlling module, configured to control the vehicle using the secondpositioning result.

In a feasible design, the first constructing module is configured todetermine a position of the GNSS receiver in the rectangular coordinatesystem according to the first positioning result; construct theconstraint condition according to the position of the GNSS receiver inthe rectangular coordinate system, where the constraint conditionsatisfies the following equations:

(X_(Lidar_fix) − X)² + (Y_(Lidar_fix) − Y)² + (Z_(Lidar_fix) − Z)² = 0$\frac{\partial X_{{Lidar}\_{fix}}}{\partial X} = 1$$\frac{\partial Y_{{Lidar}\_{fix}}}{\partial Y} = 1$${\frac{\partial Z_{{Lidar}\_{fix}}}{\partial Z} = 1};$

where X_(Lidar_fix) represents x-coordinate of the vehicle-mounted GNSSreceiver in the rectangular coordinate system, Y_(Lidar_fix) representsy-coordinate of the vehicle-mounted GNSS receiver in the rectangularcoordinate system, Z_(Lidar_fix) represents z-coordinate of thevehicle-mounted GNSS receiver in the rectangular coordinate system, Xrepresents x-coordinate of the second positioning result to be solved, Yrepresents y-coordinate of the second positioning result to be solved,and Z represents z-coordinate of the second positioning result to besolved.

In a feasible design, the positioning module is configured to constructobservation equations using the observation data; perform PPPpositioning using the observation equations in combination with theconstraint condition to obtain the second positioning result, where theobservation equations are as follows:

λφ=r+l·dx+dt·C+T+I+ε+λ·N

ρ=r+l·dx+dt·C+T−I+ε;

where ρ represents a pseudo-range between a satellite and the vehicle, Nrepresents a carrier phase integer ambiguity, r represents a distancebetween the satellite and the GNSS receiver, l represents cosine of anobservation direction, dt represents a correction amount of a clockdifference between the satellite and the vehicle-mounted GNSS receiver,T represents a tropospheric deviation, I represents an ionosphericdeviation, ε represents a noise constant, dx represents a state variableto be estimated, including a coordinate increment and a clock differencechange; and C represents a speed of light in a vacuum.

In a feasible design, the determining module is configured to convertthe point cloud data from a vehicle body coordinate system to a worldcoordinate system to obtain converted data; project the converted datato the laser point cloud reflection value map to obtain a projectionarea; determine a plurality of to-be-matched areas from the laser pointcloud reflection value map according to the projection area; determine amatching probability of the projection area and each of to-be-matchedareas to obtain a plurality of matching probabilities; determine thefirst positioning result according to the plurality of matchingprobabilities.

In a feasible design, the determining module is configured to, whendetermining the first positioning result according to the plurality ofmatching probabilities, determine a prediction probability of each ofthe plurality of matching probabilities to obtain a plurality ofprediction probabilities, where each of the prediction probabilities isa matching probability corresponding to a first positioning time beforethe current positioning time; update each matching probability of theplurality of matching probabilities using respective predictionprobabilities corresponding to the plurality of matching probabilitiesto obtain a plurality of updated matching probabilities; determine amaximum matching probability from the plurality of updated matchingprobabilities; determine the first positioning result using ato-be-matched area corresponding to the maximum matching probability.

In a feasible design, the determining module is configured to, whenupdating each matching probability of the plurality of matchingprobabilities using respective prediction probabilities corresponding tothe plurality of matching probabilities to obtain the plurality ofupdated matching probabilities, for each matching probability, determinean updated matching probability using a product of a presetnormalization coefficient, the matching probability and the predictionprobability corresponding to the matching probability.

In a feasible design, the above apparatus further includes: a secondconstructing module, configured to construct pseudo-range and carriersingle difference observation equations before the positioning moduleperforms the precise point positioning GNSS-PPP using the constraintcondition in combination with the observation data of thevehicle-mounted GNSS receiver to obtain the second positioning result;filter the observation data using the pseudo-range and carrier singledifference observation equations to filter out error data in theobservation data.

In a feasible design, the above apparatus further includes: a thirdconstructing module, configured to, before the determining moduledetermines the first positioning result using the point cloud data ofthe vehicle at the current positioning time, divide a ground plane of anearth surface in the world coordinate system into a plurality ofmap_nodes with a same size and shape, divide each map_node of theplurality of map_nodes into a plurality of map_cells with a same sizeand shape, and store corresponding map data in each map_cell of theplurality of map_cells.

In a feasible design, the map data includes at least one of thefollowing data: a mean value of laser reflection intensity values oflaser points within a positioning position corresponding to themap_cell, a variance of the laser reflection intensity values of thelaser points within the positioning position corresponding to themap_cell, and a quantity of the laser points within the positioningposition corresponding to the map_cell.

In a third aspect, an embodiment of the present application provides anelectronic device, including:

at least one processor; and

a memory communicatively connected to the at least one processor; where,

the memory stores instructions executable by the at least one processor,and the instructions are executed by the at least one processor toenable the at least one processor to execute the method of the firstaspect or any possible implementation of the first aspect.

In a forth aspect, an embodiment of the present application provides acomputer program product containing instructions, which, when running onan electronic device, causes the electronic device to execute the methodaccording to the above first aspect or various possible implementationsof the first aspect.

In a fifth aspect, an embodiment of the present application provides anon-transitory computer-readable storage medium storing computerinstructions, where the instructions are stored in the non-transitorycomputer-readable storage medium, and when running on an electronicdevice, cause the electronic device to execute the method according tothe above first aspect or various possible implementations of the firstaspect.

In a sixth aspect, an embodiment of the present application provides apositioning method, including: obtaining point cloud data using lidar ona vehicle; determining a first positioning result using the point clouddata, where the first positioning result is used to indicate a positionof the vehicle in a pre-constructed laser point cloud reflection valuemap at a current positioning time; constructing a constraint conditionusing the first positioning result so as to accelerate a convergencespeed of solving a receiver position using observation data collected bya global navigation satellite system GNSS receiver on the vehicle, toobtain a second positioning result, where the second positioning resultis used to indicate a position of the vehicle in a rectangularcoordinate system at the current positioning time.

One of the above embodiments of the application has the followingadvantages or beneficial effects: the lidar positioning technology iscombined with the GNSS-PPP positioning technology to realize the purposeof not relying on the GNSS base station; meanwhile, using thepositioning result of the lidar positioning technology, the constraintcondition that can accelerate the convergence speed of solving theposition of the receiver using the observation data can be constructed,thereby avoiding the drawback of the convergence speed being too slow.Therefore, the convergence speed of the GNSS-PPP positioning isaccelerated by fusing the lidar positioning, and the purpose of fast andaccurate positioning without relying on the GNSS base station isrealized.

Other effects of the above optional implementations will be explained inthe following in combination with specific embodiments.

BRIEF DESCRIPTION OF DRAWINGS

Accompanying drawings are used to better understand the solutions, anddo not constitute a limitation on the present application, among them:

FIG. 1 is a schematic diagram of an operating environment of apositioning method provided by an embodiment of the present application;

FIG. 2 is a flowchart of a positioning method provided by an embodimentof the present application;

FIG. 3 is a schematic process diagram of a positioning method providedby an embodiment of the present application;

FIG. 4 is a schematic diagram of calculating a matching probability in apositioning method provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of a positioning apparatusprovided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of another positioningapparatus provided by an embodiment of the present application;

FIG. 7 is a block diagram of an electronic device for implementing apositioning method of an embodiment of the present application.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments of the present application will be described belowwith reference to the accompanying drawings, including various detailsof the present application embodiments to facilitate understanding,which should be regarded as merely exemplary. Therefore, those ofordinary skill in the art should recognize that various changes andmodifications can be made to the embodiments described herein withoutdeparting from the scope and spirit of the present application. Also,for the sake of clarity and conciseness, descriptions of well-knownfunctions and structures are omitted in the following description.

In automatic driving and unmanned driving, positioning technology isparticularly important, and an accurate positioning result can ensuresafety to a great extent. Common positioning technologies include GNSSpositioning technology and lidar (Lidar) point cloud positioningtechnology, etc. The GNSS positioning technology includes GNSS-RTKpositioning technology and GNSS-PPP positioning technology. Although theGNSS-RTK positioning technology can realize positioning withcentimeter-level precision, in order to get an accurate positioningresult quickly, a large number of GNSS base stations need to bedeployed, so that GNSS base stations can be found within tens ofkilometers during driving of a vehicle. The GNSS-PPP positioningtechnology does not need to deploy a large number of GNSS base stations,but because there are many parameters to be estimated and satellitegeometry changes slowly, it takes about half an hour to converge. In thelidar point cloud positioning technology, based on Lidar point clouddata of environmental reflection, environmental features such as points,lines, surfaces, reflection values and the like are extracted, andfeature matching is performed on these environmental features with apreset environmental feature database or previous and subsequent epochenvironmental feature data, so as to obtain a global or relativeposition (pose) and realize positioning with centimeter-level precision.However, in an area with few environmental features or relatively strongtime-varying characteristics of environmental features, stability andprecision of a matching algorithm will be seriously degraded, and apositioning result with high precision and stability cannot be provided.

In view of this, embodiments of the present application provide apositioning method and an apparatus. A convergence speed of GNSS-PPPpositioning is accelerated by fusing lidar positioning, and a purpose offast and accurate positioning without relying on a GNSS base station isrealized.

FIG. 1 is a schematic diagram of an operating environment of apositioning method provided by an embodiment of the present application.Referring to FIG. 1, the operating environment includes a vehicle, avehicle-mounted lidar (Lidar) disposed on the vehicle, a vehicle-mountedGNSS receiver and a satellite, etc. Although the vehicle-mounted lidaris located at the forefront of the vehicle, in actual implementation, aposition of the vehicle-mounted lidar can be disposed flexibly, forexample, can be disposed on the top of the vehicle.

In FIG. 1, a laser point cloud reflection value map is pre-stored in amemory of the vehicle, and the laser point cloud reflection value mapmay be generated by collecting environmental features and the like ofthe earth surface or within a certain area of the earth surface using acollection vehicle in advance. During operating of the vehicle, thevehicle-mounted GNSS receiver communicates with the satellite at eachpositioning time to obtain observation data, and the observation data atleast contains a distance between the satellite and the vehicle-mountedGNSS receiver; and the GNSS receiver can also receive position-relatedinformation of the satellite in a rectangular coordinate system duringthe communication with the satellite. Meanwhile, the vehicle-mountedlidar scans surroundings of the vehicle to obtain point cloud data, andthe vehicle uses the point cloud data and the laser point cloudreflection value map to determine a first positioning result, accuracyof which is not particularly high. The vehicle uses the firstpositioning result to construct a constraint condition, and applies theconstraint condition to GNSS-PPP positioning technology, so as toaccelerate a convergence speed of solving a position of the receiverusing the observation data to obtain a GNSS-PPP positioning result(i.e., a second positioning result) and other parameters, such as aclock difference between the satellite and the GNSS receiver. In theprocess, the constraint condition is constructed using the point clouddata collected by the lidar, and the constraint condition is applied tothe GNSS-PPP positioning technology, so that the convergence speed ofsolving the position of the receiver using the observation data isaccelerated, and the purpose of fast and accurate positioning withoutrelying on a GNSS base stations is realized.

In the following, on the basis of the above FIG. 1, the positioningmethod described in the embodiments of the present application will bedescribed in detail.

FIG. 2 is a flowchart of a positioning method provided by an embodimentof the present application. This embodiment is explained from therespective of a positioning apparatus, and the positioning apparatus canbe disposed on a vehicle, and can also be disposed on a server. When thepositioning apparatus is disposed on the server, the vehicle establisheswireless network connection with the server, such as 3G connection, 4Gconnection, 5G connection, WiFi connection, Bluetooth connection, WiMAXconnection, etc. In the following, the embodiments of the presentapplication are explained by taking the positioning apparatus installedon the vehicle as an example, unless features are described. Thisembodiment includes:

101: determining a first positioning result using point cloud data of avehicle at a current positioning time.

The first positioning result is used to indicate a position of thevehicle in a pre-constructed laser point cloud reflection value map atthe current positioning time.

Exemplarily, vehicle-mounted lidar collects point cloud data and sendsthe same to a positioning apparatus at each positioning time, and thepositioning apparatus determines a relatively accurate positioningresult (hereinafter referred to as the first positioning result) usingthe point cloud data. This process includes two steps: in step 1,determining a rough predicted position with low accuracy using avehicle-mounted GNSS receiver and an inertial navigation system(Inertial Navigation System, INS) on the vehicle, for example, thepredicted position is a position with meter-level precision; in step 2,projecting the predicted position as the point cloud data onto a centerpoint O of a rectangular area M on the laser reflection value map, andfinding the first positioning result on the laser reflection value mapusing the center point O.

102: Constructing a constraint condition using the first positioningresult.

The constraint condition is used to indicate a position relationshipbetween a position of the vehicle-mounted GNSS receiver determinedaccording to the first positioning result and a second positioningresult, where the second positioning result is used to indicate aposition of the vehicle in a rectangular coordinate system at thecurrent positioning time, and a purpose of constructing the constraintcondition is to accelerate a convergence speed of solving the positionof the receiver using observation data.

103: Performing precise point positioning GNSS-PPP using the constraintcondition in combination with the observation data to obtain the secondpositioning result.

The second positioning result is used to indicate the position of thevehicle in the rectangular coordinate system at the current positioning.

Exemplarily, the essence of GNSS-PPP positioning technology is to trainobservation equations in advance, where the observation equationsinclude some observation variables and some parameters to be estimated,the observation variables at each positioning time are known, and theparameters to be estimated include a GNSS-PPP positioning result(hereinafter referred to as the second positioning result). Therefore,the observation equations can be solved according to the observationvariables at different positioning times within a period of time, andthen the parameters to be estimated can be determined, where the periodof time is called convergence time. In the prior art, because there aremany parameters to be estimated, the convergence time is too long, andthen the convergence speed is low. Therefore, in the embodiment of thepresent application, the positioning apparatus constructs, according tothe point cloud data collected by the vehicle-mounted lidar, theconstraint condition used to accelerate the convergence speed of solvingthe position of the receiver using the observation data, and thenquickly determines the parameters to be estimated including the secondpositioning result.

104: Controlling the vehicle using the second positioning result.

In the embodiment of the present application, compared with the firstpositioning result, accuracy of the second positioning result is higherand more accurate. Therefore, according to the second positioningresult, the operating of the vehicle can be controlled more accuratelyand traffic safety can be improved.

According to the positioning method provided by the embodiment of thepresent application, the first positioning result is determined usingthe point cloud data collected by the lidar in combination with thelaser point cloud reflection value map; the constraint condition used toaccelerate the convergence speed of solving the position of the receiverusing the observation data is constructed using the first positioningresult; the GNSS-PPP positioning is performed using the constraintcondition in combination with the observation data of the GNSS receiverto obtain the second positioning result. Using this solution, lidarpositioning technology is combined with the GNSS-PPP positioningtechnology to realize a purpose of not relying on a GNSS base station.Meanwhile, using the positioning result of the lidar positioningtechnology, the constraint condition that can accelerate the convergencespeed of solving the position of the receiver using the observation datacan be constructed, thereby avoiding a drawback of the convergence speedbeing too slow. Therefore, the convergence speed of GNSS-PPP positioningis accelerated by fusing lidar positioning, and a purpose of fast andaccurate positioning without relying on the GNSS base station isrealized.

According to the above, the positioning method provided by theembodiment of the present application is mainly to combine the lidarpositioning technology with the GNSS-PPP positioning technology, and thelidar positioning technology needs to use the laser reflection valuemap, which can be constructed offline in advance and loaded on thevehicle. It can be seen that the positioning method provided by theembodiment of the present application includes three parts: constructingthe laser reflection value map, determining the first positioning resultbased on the lidar positioning technology, and performing the GNSS-PPPpositioning based on the observation data, where the GNSS-PPPpositioning based on the observation data uses the constraint condition.Exemplarily, please refer to FIG. 3, FIG. 3 is a schematic processdiagram of a positioning method provided by an embodiment of the presentapplication. Next, the three parts will be described in detail withreference to FIG. 3.

First, constructing the laser reflection value map offline.

In a feasible implementation, the construction of the laser reflectionvalue map can be realized as follows: dividing a ground plane of anearth surface in a world coordinate system into a plurality of map_nodeswith the same size and shape; dividing each map_node of the plurality ofregional areas into a plurality of map_cells with the same size andshape, and storing corresponding map data in each map_cell of theplurality of map_cells. The map data includes a mean value of laserreflection intensity values of laser points within a positioningposition corresponding to the map_cell, a variance of the laserreflection intensity values of the laser points within the positioningposition corresponding to the map_cell, and a quantity of the laserpoints within the positioning position corresponding to the map_cell.

Exemplarily, this step is an offline preprocessing process. In thepreprocessing process, the whole world coordinate system is divided intoblocks with fixed size and shape (map_nodes), which can also be calledregional areas, and each map_node covers a certain range, where theworld coordinate system is, for example, the universal transversemercator (Universal Transverse Mercator, UTM) coordinate system. In thisway, when the size and arrangement rule of the map_nodes are known, amap_node where one coordinate is located can be calculated according tothe coordinate, that is, the map_node where the coordinate is located.After dividing the world coordinate system into a plurality of regionalareas, each map_node is divided into m×n map_cells (map_cells), forexample, each map_node is divided into 1024×1024 map_cells. After that,point cloud data that fall into the same map_cell in the point clouddata collected by a collection vehicle are aggregated, and each map_cellstores the aggregated data, so that the data amount in each map_node isfixed regardless of the number of laser points. Map data correspondingto each map_cell is stored in each map_cell, and the map data includes amean value of laser reflection intensity values of laser points within apositioning position corresponding to this map_cell, a variance of thelaser reflection intensity values of the laser points within thepositioning position corresponding to this map_cell, and the quantity ofthe laser points within the positioning position corresponding to thismap_cell.

Using this solution, a purpose of constructing the laser reflectionvalue map offline is realized.

Second, determining the first positioning result based on the Lidarpositioning technology.

Please refer to FIG. 3, this part includes the following steps:

201: obtaining Lidar point cloud data.

The Lidar point cloud data is the point cloud data in the above step101. The vehicle-mounted lidar scans the surroundings of the vehicle ateach positioning time to generate the point cloud data.

202: Converting the point cloud data.

Exemplarily, the point cloud data obtained by the vehicle-mounted lidaris located in a vehicle body coordinate system, and the laser reflectionvalue map is based on the world coordinate system. In order to match thepre-made laser reflection value map, it is necessary to process thepoint cloud data obtained in real time according to an organization formof laser reflection value map data, that is, to perform map_nodedivision on the point cloud data, and further perform map_cell divisionon map_nodes. Therefore, at each positioning time, it is necessary toconvert the point cloud data from the vehicle body coordinate system tothe world coordinate system, and perform the map_node division and themap_cell division to obtain the converted data.

203: Calculating a matching probability of a point cloud reflectionvalue.

Exemplarily, please refer to FIG. 4, FIG. 4 is a schematic diagram ofcalculating a matching probability in a positioning method provided byan embodiment of the present application. Referring to FIG. 4, a lowercell with large area is a map_node contained in the laser reflectionvalue map, and the upper M is a rectangular area where the point clouddata is projected on the laser reflection value map. The positioningapparatus determines a rough predicted position using an INS on thevehicle, and the predicted position is used to indicate an approximateposition of the vehicle in the laser reflection value map, such as thecenter point O on the rectangular area M in FIG. 4. After obtaining thepredicted position, a projection area corresponding to the point clouddata can be determined on the laser reflection value map. Ten, accordingto the projection area, a plurality of to-be-matched areas can bedetermined from the laser point cloud reflection value map; matchingprobabilities between the projection area and the to-be-matched areascan be determined to obtain a plurality of matching probabilities; andthe first positioning result can be determined according to theplurality of matching probabilities.

Please refer to FIG. 4 again, the rectangular area M is an areacontaining 5×5 map_cells, and each map_cell records the mean value andvariance of laser reflection intensity values of laser points within apositioning position corresponding to this map_cell as well as thequantity of the laser points. Because the point O is not accurate,re-estimation is needed. In the estimation process, firstly, a map_node(map_node) and a map_cell (hereinafter referred to as a first map_cell)where the center point O is located are determined from the laserreflection value map using the center point O. This map_cell andmap_cells within a certain range around this map_cell (hereinafterreferred to as second map_cells) are taken as map_cells that may matchthe first map_cell, and the second map_cells are marked with 1, 2, 3, 4,5, 6, . . . 16 in the figure. Then, from the laser reflection value mapand by taking each second map_cell as a center, a plurality ofrectangular areas with the same size and shape as the rectangular area Mare determined as to-be-matched areas, that is, 16 to-be-matched areas.After that, matching probabilities of the rectangular area M and theto-be-matched areas are calculated to obtain 16 matching probabilities.At last, the first positioning result is determined according to theplurality of matching probabilities.

The following formula (1) can be used to calculate a matchingprobability of the center point O of M and a map point (x, y):

$\begin{matrix}{\frac{\sum\limits_{i,j}{{{\mu_{{i - x},{j - y}}^{m} - \mu_{i,j}^{r}}} \cdot N_{i,j}^{r}}}{\sum\limits_{i,j}N_{i,j}^{r}}{{{P\left( {{z❘x},y} \right)} = \alpha};}} & (1)\end{matrix}$

where (x, y) represents a center point of a to-be-matched area, (i, j)represents coordinates of a small cell in the to-be-matched area, zrepresents all measured values of a current frame, P(z|x, y) is amatching probability that the vehicle is currently in the to-be-matchedarea, and that is estimated according to the measured values of thecurrent frame, In the measured values, μ_(i-x,j-y) ^(m) represents amean value of reflection values of the cell (x, y) in the to-be-matchedarea, μ_(i,j) ^(r) represents a mean value of laser reflection intensityvalues within the rectangular area M; N_(i,j) ^(r) represents thequantity of laser points in the rectangular area M, and a is a constant.By still taking 16 to-be-matched areas as an example, because (x, y) ofthe 16 to-be-matched areas are different, 16 P(z|x, y), i.e. 16 matchingprobabilities can be obtained using the above formula (1).

In the embodiment of the present application, after calculating aplurality of matching probabilities using the formula (1), a maximummatching probability can be determined from these matchingprobabilities, and the first positioning result can be determined usinga to-be-matched area corresponding to the maximum matching probability.Optionally, in order to further improve the accuracy, these matchingprobabilities can also be updated, a maximum matching probability isdetermined from the updated matching probabilities, and the firstpositioning result is determined using a to-be-matched areacorresponding to the maximum matching probability. Exemplarily, pleaserefer to step 204.

204: Updating the matching probabilities based on a histogram filter.

In the process of updating the matching probabilities, a predictionprobability of each of the plurality of matching probabilities isdetermined to obtain a plurality of prediction probabilities. Theprediction probability is a matching probability corresponding to afirst positioning time before the current positioning time. Eachmatching probability of the plurality of matching probabilities isupdated using respective prediction probabilities corresponding to theplurality of matching probabilities, to obtain a plurality of updatedmatching probabilities.

Exemplarily, P(z−x, y) calculated by the formula (1) in the above step203 is the matching probability of a to-be-matched area and therectangular area M that is estimated according to the measured values ofthe current frame. Meanwhile, the positioning apparatus maintains aprediction probability P(x, y), and the P(x, y) refers to a probabilitythat the vehicle is located in the to-be-matched area and that isdeduced only according to historical values without considering themeasured values of the current frame. After obtaining a plurality ofmatching probabilities using the above step 203, the formula (2) is usedto update each matching probability:

P(x,y)=ηP(z|x,y) P (x,y)  (2);

where P(x, y) represents an updated probability after updating theP(z|x, y), and η represents a normalization coefficient. Continuing withthe above example, when there are 16 to-be-matched areas, 16 updatedmatching probabilities can be obtained using the formula (2).

205: Calculating the first positioning result based on the updatedmatching probabilities.

Exemplarily, the positioning apparatus determines a maximum matchingprobability from the plurality of updated matching probabilities, anddetermines the first positioning result using a to-be-matched areacorresponding to the maximum matching probability. Assuming that themaximum matching probability is P_(max) (x, y) among the updatedmatching probabilities in step 204 above, the first positioning resultcan be determined using formula (3). The formula (3) is as follows:

$\begin{matrix}{{\overset{\_}{x} = \frac{\sum\limits_{x,y}{{P_{\max}\left( {x,y} \right)}^{\alpha} \cdot x}}{\sum\limits_{x,y}{P_{\max}\left( {x,y} \right)}^{\alpha}}},{{\overset{\_}{y} = \frac{\sum\limits_{x,y}{{P_{\max}\left( {x,y} \right)}^{\alpha} \cdot y}}{\sum\limits_{x,y}{P_{\max}\left( {x,y} \right)}^{\alpha}}};}} & (3)\end{matrix}$

where x, y respectively represents abscissa and ordinate in the firstpositioning result, and a is a constant parameter.

Using this solution, a purpose of determining the position of thevehicle in the reflection value map of laser point cloud in real time isrealized.

Finally, performing GNSS-PPP positioning based on the observation data.

Please refer to FIG. 3, this part includes the following steps:

301: obtaining observation data.

Exemplarily, the vehicle-mounted GNSS receiver on the vehicle can obtainthe observation data from the satellite.

302: Performing cycle slip detection and repair based on carrier timedifference.

Exemplarily, the cycle slip detection and repair can be performed forthe GNSS receiver using the predicted position. In actualimplementation, when using the observation data corresponding to eachsatellite for positioning, cycle slip detection and displacementestimation can be carried out first based on the carrier timedifference. For example, pseudo-range and carrier single differenceobservation equations can be constructed between adjacent epochs (i.e.,a previous positioning time and the current positioning time), and thetwo equations are as shown in formula (4):

ρ_(k,k-1) ^(i) =r _(k,k-1) ^(i) +l _(k,k-1) ^(i) ·dx+dt _(k,k-1) ·C+T_(k,k-1) ^(i) +I _(k,k-1) ^(i)+ε

λφ_(k,k-1) ^(i) +λ·N _(k,k-1) ^(i) =r _(k,k-1) ^(i) +l _(k,k-1) ^(i)·dx+dt _(k,k-1) ^(i) ·C+T _(k,k-1) ^(i) −I _(k,k-1) ^(i)+ε  (4);

where k−1 represents the previous positioning time, k represents thecurrent positioning time, i represents a satellite identification,ρ_(k,k-1) ^(i) represents a change value of a satellite-groundpseudo-range, r_(k,k-1) ^(i) represents a change value of asatellite-ground distance, dt_(k,k-1) ^(i) represents a correctionamount of a clock difference between the satellite and thevehicle-mounted GNSS receiver, T_(k,k-1) ^(i) represents an observationdistance deviation caused by a change of a tropospheric delay, I_(k,k-1)^(i) represents an observation distance deviation caused by a change ofan ionospheric delay, ε represents a noise constant, φ_(k,k-1) ^(i)represents a phase change observed by the GNSS receiver, N_(k,k-1) ^(i)represents a cycle slip value of a carrier phase, I_(k,k-1) ^(i)represents cosine of an observation direction, and dx represents a statevariable to be estimated, including a coordinate increment and a clockdifference change; and C represents a speed of light in a vacuum.

When an update frequency of the GNSS receiver is relatively large, suchas 10 Hz, it can be consider that the troposphere and ionosphere havelittle change in one update. Let the ionosphere error and thetroposphere error in the formula (4) be equal to 0, then formula (5) canbe obtained:

ρ_(k,k-1) ^(i) =r _(k,k-1) ^(i) +l _(k,k-1) ^(i) ·dx+dt _(k,k-1) ·C+ε

λφ_(k,k-1) ^(i) +λ·N _(k,k-1) ^(i) =r _(k,k-1) ^(i) +l _(k,k-1) ^(i)·dx+dt _(k,k-1) ^(i) ·C+ε  (5);

a value of N_(k,k-1) ^(i) can be obtained by calculating using theformula (5), and if no jump occurs, N_(k,k-1) ^(i)=0.

Based on the formula (5), robust estimation is used to estimate dx anddt_(k,k-1), and a carrier phase observation value with cycle slip isrepaired.

By taking the update frequency of the GNSS receiver being 10 Hz as anexample, the GNSS receiver collects observation data every 0.1 seconds,and there may be inaccurate data in the observation data collected eachtime due to jump and other reasons. Therefore, it is possible to performsubtraction and error elimination on the observation data through theformula (4) and the formula (5), preprocess continuous observation data,and then construct observation equations using preprocessed observationdata, that is, execute step 303.

303: Constructing GNSS observation equations.

Exemplarily, the observation equations shown in formula (6) areconstructed using the preprocessed observation data obtained in theabove step 302:

λφ=r+l·dx+dt·C+T+I+ε+λ·N

ρ=r+l·dx+dt·C+T−I+ε  (6)

The positioning apparatus constitutes observation equations according tothe observation data of the vehicle-mounted GNSS receiver,high-precision satellite clock error and orbit, and a product of phaseand code deviation correction.

In the above formulas (4), (5) and (6), on the left side of the equalsign are known observation variables, and on the right side are theparameters to be solved, where r is a distance between the positioningresult (i.e. the second positioning result to be solved) obtained byGNSS-PPP and the satellite. Assuming that the second positioning resultis represented as (X, Y, Z) in the rectangular coordinate system, thenthe distance r between the satellite identified as i and the GNSSreceiver in the formula (6) can be obtained using satellite coordinatesof the second positioning result. Assuming that coordinates of thesatellite identified as i in the rectangular coordinate system is(X_(i),Y_(i),Z_(i)), then r=(X−X_(i))²+(Y−Y_(i))²+(Z−Z_(i))². It can beseen that the existing GNSS-PPP technology can obtain, according to theformula (6), the second positioning result after convergence for aperiod of time, but the convergence speed is slow because there are manyparameters to be solved on the right side of the equal sign of theformula (6).

In order to accelerate the convergence speed, the embodiment of thepresent application introduces constraint equations, that is, step 304is executed.

304: Adding constraint equations.

Exemplarily, the positioning apparatus determines a position of the GNSSreceiver in the rectangular coordinate system according to the firstpositioning result, and constructs the constraint condition according tothe position of the GNSS receiver in the rectangular coordinate system,where the constraint condition satisfies the following formula (7):

$\begin{matrix}{{{\left( {X_{{Lidar}\_{fix}} - X} \right)^{2} + \left( {Y_{{Lidar}\_{fix}} - Y} \right)^{2} + \left( {Z_{{Lidar}\_{fix}} - Z} \right)^{2}} = 0}{\frac{\partial X_{{Lidar}\_{fix}}}{\partial X} = 1}{\frac{\partial Y_{{Lidar}\_{fix}}}{\partial Y} = 1}{{\frac{\partial Z_{{Lidar}\_{fix}}}{\partial Z} = 1};}} & (7)\end{matrix}$

where X_(Lidar_fix), Y_(Lidar_fix), Z_(Lidar_fix) represent the positionof the GNSS receiver in the rectangular coordinate system, and X, Y, Zrepresent the second positioning result.

Exemplarily, the first positioning result is obtained based on the aboveformula (3). The first positioning result is located in the worldcoordinate system, such as the UTM coordinate system, and is planecoordinates. The first positioning result can be converted into therectangular coordinate system by combining the first positioning resultwith a height difference and the like, and the position (X_(Lidar_fix),Y_(Lidar_fix), Z_(Lidar_fix)) of the GNSS receiver in the rectangularcoordinate system can be obtained.

305: Performing GNSS-PPP positioning to obtain the second positioningresult.

Exemplarily, after obtaining the constraint equations (7), under theconstraint of the constraint equations (7), the positioning apparatusmakes the formula (6) converge quickly using the observation data, so asto obtain the second positioning result and other parameters.

Using this solution, a purpose of constructing the constraint conditionfor accelerating the convergence speed of solving the position of thereceiver using the observation data is realized.

FIG. 5 is a schematic structural diagram of a positioning apparatusprovided by an embodiment of the present application. The apparatus canbe integrated in or realized by an electronic device, and the electronicdevice can be a terminal device or a server. As shown in FIG. 5, in thisembodiment, the positioning apparatus 100 can include:

a determining module 11, configured to determine a first positioningresult using point cloud data of a vehicle at a current positioningtime, where the first positioning result is used to indicate a positionof the vehicle in a pre-constructed laser point cloud reflection valuemap at the current positioning time;

a first constructing module 12, configured to construct a constraintcondition using the first positioning result, where the constraintcondition is used to indicate a position relationship between a positionof a vehicle-mounted GNSS receiver determined according to the firstpositioning result and a second positioning result, and the secondpositioning result is used to indicate a position of the vehicle in arectangular coordinate system at the current positioning time;

a positioning module 13, configured to perform precise point positioningGNSS-PPP using the constraint condition in combination with observationdata of the vehicle-mounted GNSS receiver to obtain the secondpositioning result;

a controlling module 14, configured to control the vehicle using thesecond positioning result.

In a feasible design, the first constructing module 12 is configured todetermine a position of the GNSS receiver in the rectangular coordinatesystem according to the first positioning result; construct theconstraint condition according to the position of the GNSS receiver inthe rectangular coordinate system, where the constraint conditionsatisfies the following equations:

(X_(Lidar_fix) − X)² + (Y_(Lidar_fix) − Y)² + (Z_(Lidar_fix) − Z)² = 0$\frac{\partial X_{{Lidar}\_{fix}}}{\partial X} = 1$$\frac{\partial Y_{{Lidar}\_{fix}}}{\partial Y} = 1$${\frac{\partial Z_{{Lidar}\_{fix}}}{\partial Z} = 1};$

where X_(Lidar_fix) represents x-coordinate of the vehicle-mounted GNSSreceiver, Y_(Lidar_fix) represents y-coordinate of the vehicle-mountedGNSS receiver, Z_(Lidar_fix) represents z-coordinate of thevehicle-mounted GNSS receiver, X represents x-coordinate of the secondpositioning result to be solved, Y represents y-coordinate of the secondpositioning result to be solved, and Z represents z-coordinate of thesecond positioning result to be solved.

In a feasible design, the positioning module 13 is configured toconstruct observation equations using the observation data; perform PPPpositioning using the observation equations in combination with theconstraint condition to obtain the second positioning result, where theobservation equations are as follows:

λφ=r+l·dx+dt·C+T+I+ε+λ·N

ρ=r+l·dx+dt·C+T−I+ε;

where ρ represents a pseudo-range between a satellite and the vehicle, Nrepresent a carrier phase integer ambiguity, r represents a distancebetween the satellite and the GNSS receiver, l represents cosine of anobservation direction, dt represents a correction amount of a clockdifference between the satellite and the vehicle-mounted GNSS receiver,T represents a tropospheric deviation, I represents an ionosphericdeviation, ε represents a noise constant, dx represents a state variableto be estimated, including a coordinate increment and a clock differencechange; and C represents a speed of light in a vacuum.

In a feasible design, the determining module 11 is configured to convertthe point cloud data from a vehicle body coordinate system to a worldcoordinate system to obtain converted data; project the converted datato the laser point cloud reflection value map to obtain a projectionarea; determine a plurality of to-be-matched areas from the laser pointcloud reflection value map according to the projection area; determine amatching probability of the projection area and each of to-be-matchedareas to obtain a plurality of matching probabilities; determining thefirst positioning result according to the plurality of matchingprobabilities.

In a feasible design, the determining module 11 is configured to, whendetermining the first positioning result according to the plurality ofmatching probabilities, determine a prediction probability of each ofthe plurality of matching probabilities to obtain a plurality ofprediction probabilities, where each of the prediction probabilities isa matching probability corresponding to a first positioning time beforethe current positioning time; update each matching probability of theplurality of matching probabilities using respective predictionprobabilities corresponding to the plurality of matching probabilitiesrespectively to obtain a plurality of updated matching probabilities;determine a maximum matching probability from the plurality of updatedmatching probabilities; determine the first positioning result using ato-be-matched area corresponding to the maximum matching probability.

In a feasible design, the determining module 11 is configured to, whenupdating each matching probability of the plurality of matchingprobabilities using respective prediction probabilities corresponding tothe plurality of matching probabilities to obtain the plurality ofupdated matching probabilities, for each matching probability, determinean updated matching probability using a product of a presetnormalization coefficient, the matching probability and the predictionprobability corresponding to the matching probability.

FIG. 6 is a schematic structural diagram of another positioningapparatus provided by an embodiment of the present application.Referring to FIG. 6, on the basis of the above FIG. 6, the positioningapparatus 100 provided in this embodiment further includes:

a second constructing module 15, configured to construct pseudo-rangeand carrier single difference observation equations before thepositioning module 13 performs the precise point positioning GNSS-PPPusing the constraint condition in combination with the observation dataof the vehicle-mounted GNSS receiver to obtain the second positioningresult; filter the observation data using the pseudo-range and carriersingle difference observation equations to filter out error data in theobservation data.

Please refer to FIG. 6, in a feasible design, the above positioningapparatus further includes: a third constructing module 16, configuredto, before the determining module 11 determines the first positioningresult using the point cloud data of the vehicle at the currentpositioning time, divide a ground plane of an earth surface in the worldcoordinate system into a plurality of map_nodes with a same size andshape; divide each map_node of the plurality of regional areas into aplurality of map_cells with a same size and shape, and storecorresponding map data in each map_cell of the plurality of map_cells.

In a feasible design, the map data includes at least one of thefollowing data: a mean value of laser reflection intensity values oflaser points within a positioning position corresponding to themap_cell, a variance of the laser reflection intensity values of thelaser points within the positioning position corresponding to themap_cell, and a quantity of the laser points within the positioningposition corresponding to the map_cell.

The apparatus provided in the embodiment of the present application canbe used for the method performed by the vehicle in the above embodiment.Implementation principles and technical effects thereof are similar, andwill not be repeated here.

The present application also provides an electronic device and areadable storage medium according to embodiments of the presentapplication.

FIG. 7 is a block diagram of an electronic device for implementing apositioning method of an embodiment of the present application. As shownin FIG. 7, it is a block diagram of the electronic device for thepositioning method according to the embodiment of the presentapplication. The electronic device is intended to represent variousforms of digital computers, such as a laptop computer, a desktopcomputer, a workstation, a personal digital assistant, a server, a bladeserver, a mainframe computer, and other suitable computers. Theelectronic device can also represent various forms of mobileapparatuses, such as a personal digital assistant, a cellular phone, asmart phone, a wearable devices and other similar computing apparatuses.The components shown herein, their connections and relationships, andtheir functions are merely examples, and are not intended to limit theimplementation of the present application described and/or claimedherein.

As shown in FIG. 7, the electronic device includes one or moreprocessors 21, a memory 22, and interfaces for connecting variouscomponents, including a high-speed interface and a low-speed interface.The various components are connected to each other by different buses,and can be mounted on a common main board or in other ways as required.The processor can process instructions executed within the electronicdevice, including instructions stored in or on the memory to displaygraphical information of a GUI on an external input/output apparatus,such as a display device coupled to an interface. In other embodiments,multiple processors and/or multiple buses may be used with multiplememories, if desired. Similarly, multiple electronic devices can beconnected, and each device provides some necessary operations (forexample, as a server array, a group of blade servers, or amultiprocessor system). In FIG. 7, one processor 21 is taken as anexample.

The memory 22 is the non-transitory computer-readable storage mediumprovided in the present application. The memory stores instructionsexecutable by at least one processor, so that the at least one processorexecutes the positioning method provided in the present application. Thenon-transitory computer-readable storage medium of the presentapplication stores computer instructions for causing a computer toexecute the positioning method provided in the present application.

As a non-transitory computer readable storage medium, the memory 22 canbe used to store non-transitory software programs, non-transitorycomputer-executable programs and modules, such as programinstructions/modules corresponding to the positioning method in theembodiments of the present application (for example, the determiningmodule 11, the first constructing module 12, the positioning module 13and the controlling module 14 shown in FIG. 5, and the secondconstructing module 15 and the third constructing module 16 shown inFIG. 6). The processor 21 executes various functional applications anddata processing of the server by running non-instantaneous softwareprograms, instructions and modules stored in the memory 22, that is,realizes the positioning method in the above method embodiments.

The memory 22 may include a program storage area and a data storagearea, where the program storage area may store an application programrequired by an operating system and at least one function; the datastorage area may store data created according to the use of thepositioning electronic device, etc. In addition, the memory 22 mayinclude a high-speed random access memory, and may also include anon-transitory memory, such as at least one disk memory device, a flashmemory device, or other non-transitory solid-state memory devices. Insome embodiments, the memory 22 may optionally include memories disposedremotely with respect to the processor 21, and these remote memories maybe connected to the positioning electronic device through a network.Examples of the above network include, but are not limited to, theInternet, an intranet, a local area network, a mobile communicationnetwork and combinations thereof.

The electronic device for the positioning method can further include aninput apparatus 23 and an output apparatus 24. The processor 21, thememory 22, the input apparatus 23, and the output apparatus 24 may beconnected through a bus or other ways. A connection through a bus istaken as an example in FIG. 7.

The input apparatus 23 can receive inputted digital or characterinformation and generate key signal input related to user setting andfunction control of the positioning electronic device, such as a touchscreen, a keypad, a mouse, a track pad, a touch pad, an indicator stick,one or more mouse buttons, a trackball, a joystick and other inputapparatuses. The output apparatus 24 can include a display device, anauxiliary lighting apparatus (e.g., an LED), a tactile feedbackapparatus (e.g., a vibration motor), and the like. The display devicemay include, but is not limited to, a liquid crystal display (LCD), alight emitting diode (LED) display, and a plasma display. In someembodiments, the display device may be a touch screen.

Various embodiments of the systems and techniques described herein maybe implemented in a digital electronic circuit system, an integratedcircuit systems, an application specific ASIC (application specificintegrated circuit), computer hardware, firmware, software, and/orcombinations thereof. These various embodiments may include beingimplemented in one or more computer programs that can be executed and/orinterpreted on a programmable system including at least one programmableprocessor, which can be a special purpose or general purposeprogrammable processor, and can receive data and instructions from, andtransmit data and instructions to, a storage system, at least one inputapparatus, and at least one output apparatus.

These computing programs (also called programs, software, softwareapplications, or codes) include machine instructions of a programmableprocessor, and can be implemented using high-level procedures and/orobject-oriented programming languages, and/or assembly/machinelanguages. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,device, and/or apparatus (e.g., a magnetic disk, an optical disk, amemory, a programmable logic device (PLD)) for providing machineinstructions and/or data to a programmable processor, including amachine-readable medium that receives machine instructions asmachine-readable signals. The term “machine readable signal” refers toany signal used to provide machine instructions and/or data to aprogrammable processor.

To provide interaction with a user, the systems and techniques describedherein can be implemented on a computer having; a display apparatus(e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor)for displaying information to users; and a keyboard and a pointingapparatus (e.g., a mouse or a trackball) through which the user canprovide inputs to the computer. Other kinds of apparatuses can also beused to provide interaction with the user. For example, a feedbackprovided to the user can be any form of sensory feedback (for example,visual feedback, auditory feedback, or tactile feedback); and inputsfrom the user can be received in any form, including acoustic input,voice input or tactile input.

The systems and techniques described herein can be implemented in acomputing system including background components (e.g., as a dataserver), a computing system including middleware components (e.g., anapplication server), or a computing system including front-endcomponents (e.g., a user computer with a graphical user interface or aweb browser through which a user can interact with embodiments of thesystems and techniques described herein), or a computing systemincluding any combination of such background components, middlewarecomponents and front-end components. Components of the system can beconnected to each other through digital data communication in any formor medium (e.g., a communication network). Examples of the communicationnetwork include a local area network (LAN), a wide area networks (WAN),and the Internet.

A computer system may include a client and a server. The client and theserver are generally remote from each other and usually interact througha communication network. A relationship between the client and theserver is generated by computer programs running on correspondingcomputers and having a client-server relationship with each other.

An embodiment of the present application also provides a positioningmethod. In the method: obtaining point cloud data using lidar on avehicle; determining a first positioning result using the point clouddata, where the first positioning result is used to indicate a positionof the vehicle in a pre-constructed laser point cloud reflection valuemap at the current positioning time; constructing a constraint conditionusing the first positioning result to accelerate a convergence speed ofsolving a receiver position using observation data, to obtain a secondpositioning result, where the second positioning result is used toindicate a position of the vehicle in a rectangular coordinate system atthe current positioning.

According to the technical solutions of the embodiments of the presentapplication, the lidar positioning technology is combined with theGNSS-PPP positioning technology to realize the purpose of not relying onthe GNSS base station. Meanwhile, a construction using the positioningresult of the lidar positioning technology can accelerate theconvergence speed of the observation data of the GNSS receiver and avoidthe drawback of the convergence speed being too slow. Therefore, theconvergence speed of the GNSS-PPP positioning is accelerated by fusingthe lidar positioning, and the purpose of fast and accurate positioningwithout relying on the GNSS base stations is realized.

It should be understood that steps may be reordered, added or deletedfor the various forms of processes shown above. For example, the stepsdescribed in the present application can be performed in parallel,sequentially, or in a different order, as long as the desired results ofthe technical solutions disclosed in the present application can berealized. This is not limited herein.

The above specific implementations do not constitute a limitation on theprotection scope of the present application. Those skilled in the artshould understand that various modifications, combinations,sub-combinations and substitutions can be made according to designrequirements and other factors. Any amendments, equivalent substitutionsand improvements made within the spirit and principles of the presentapplication shall be included within the protection scope of the presentapplication.

What is claimed is:
 1. A positioning method, comprising: determining a first positioning result using point cloud data of a vehicle at a current positioning time, wherein the first positioning result is used to indicate a position of the vehicle in a pre-constructed laser point cloud reflection value map at the current positioning time; constructing a constraint condition using the first positioning result, wherein the constraint condition is used to indicate a position relationship between a position of a vehicle-mounted Global Navigation Satellite System (GNSS) receiver determined according to the first positioning result and a second positioning result, and the second positioning result is used to indicate a position of the vehicle in a rectangular coordinate system at the current positioning time; performing precise point positioning (PPP) using the constraint condition in combination with observation data of the vehicle-mounted GNSS receiver to obtain the second positioning result; controlling the vehicle using the second positioning result.
 2. The method according to claim 1, wherein the constructing the constraint condition using the first positioning result comprises: determining a position of the GNSS receiver in the rectangular coordinate system according to the first positioning result; constructing the constraint condition according to the position of the GNSS receiver in the rectangular coordinate system, wherein the constraint condition satisfies the following equations: (X_(Lidar_fix) − X)² + (Y_(Lidar_fix) − Y)² + (Z_(Lidar_fix) − Z)² = 0 $\frac{\partial X_{{Lidar}\_{fix}}}{\partial X} = 1$ $\frac{\partial Y_{{Lidar}\_{fix}}}{\partial Y} = 1$ ${\frac{\partial Z_{{Lidar}\_{fix}}}{\partial Z} = 1};$ wherein X_(Lidar_fix) represents x-coordinate of the vehicle-mounted GNSS receiver in the rectangular coordinate system, Y_(Lidar_fix) represents y-coordinate of the vehicle-mounted GNSS receiver in the rectangular coordinate system, Z_(Lidar_fix) represents z-coordinate of the vehicle-mounted GNSS receiver in the rectangular coordinate system, X represents x-coordinate of the second positioning result to be solved, Y represents y-coordinate of the second positioning result to be solved, and Z represents z-coordinate of the second positioning result to be solved.
 3. The method according to claim 2, wherein the performing the precise point positioning (PPP) using the constraint condition and the observation data of the vehicle-mounted GNSS receiver to obtain the second positioning result comprises: constructing observation equations using the observation data; performing PPP positioning using the observation equations in combination with the constraint condition to obtain the second positioning result, wherein the observation equations are as follows: λφ=r+l·dx+dt·C+T+I+ε+λ·N ρ=r+l·dx+dt·C+T−I+ε; wherein ρ represents a pseudo-range between a satellite and the vehicle, N represents a carrier phase integer ambiguity, r represents a distance between the satellite and the GNSS receiver, l represents cosine of an observation direction, dt represents a correction amount of a clock difference between the satellite and the vehicle-mounted GNSS receiver, T represents a tropospheric deviation, I represents an ionospheric deviation, ε represents a noise constant, dx represents a state variable to be estimated, comprising a coordinate increment and a clock difference change, and C represents a speed of light in a vacuum.
 4. The method according to claim 1, wherein the determining the first positioning result using the point cloud data of the vehicle at the current positioning time comprises: converting the point cloud data from a vehicle body coordinate system to a world coordinate system to obtain converted data; projecting the converted data to the laser point cloud reflection value map to obtain a projection area; determining a plurality of to-be-matched areas from the laser point cloud reflection value map according to the projection area; determining a matching probability of the projection area and each of the to-be-matched areas to obtain a plurality of matching probabilities; determining the first positioning result according to the plurality of matching probabilities.
 5. The method according to claim 4, wherein the determining the first positioning result according to the plurality of matching probabilities comprises: determining a prediction probability of each of the plurality of matching probabilities to obtain a plurality of prediction probabilities, wherein each of the prediction probabilities is a matching probability corresponding to a first positioning time before the current positioning time; updating each matching probability of the plurality of matching probabilities using respective prediction probabilities corresponding to the plurality of matching probabilities to obtain a plurality of updated matching probabilities; determining a maximum matching probability from the plurality of updated matching probabilities; determining the first positioning result using a to-be-matched area corresponding to the maximum matching probability.
 6. The method according to claim 5, wherein the updating each matching probability of the plurality of matching probabilities using respective prediction probabilities corresponding to the plurality of matching probabilities to obtain the plurality of updated matching probabilities comprises: for each matching probability, determining an updated matching probability using a product of a preset normalization coefficient, the matching probability and the prediction probability corresponding to the matching probability.
 7. The method according to claim 1, before performing the precise point positioning GNSS-PPP using the constraint condition in combination with the observation data of the vehicle-mounted GNSS receiver to obtain the second positioning result, further comprising: constructing pseudo-range and carrier single difference observation equations; filtering the observation data using the pseudo-range and carrier single difference observation equations to filter out error data in the observation data.
 8. The method according to claim 1, before determining the first positioning result using the point cloud data of the vehicle at the current positioning time, further comprising: dividing a ground plane of an earth surface in a world coordinate system into a plurality of map_nodes with a same size and shape; dividing each map_node of the plurality of map_nodes into a plurality of map_cells with a same size and shape; storing corresponding map data in each map_cell of the plurality of map_cells.
 9. The method according to claim 8, wherein the map data comprises at least one of the following data: a mean value of laser reflection intensity values of laser points within a positioning position corresponding to the map_cell, a variance of the laser reflection intensity values of the laser points within the positioning position corresponding to the map_cell, and a quantity of the laser points within the positioning position corresponding to the map_cell.
 10. A positioning apparatus, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: determine a first positioning result using point cloud data of a vehicle at a current positioning time, wherein the first positioning result is used to indicate a position of the vehicle in a pre-constructed laser point cloud reflection value map at the current positioning time; construct a constraint condition using the first positioning result, wherein the constraint condition is used to indicate a position relationship between a position of a vehicle-mounted Global Navigation Satellite System (GNSS) receiver determined according to the first positioning result and a second positioning result, and the second positioning result is used to indicate a position of the vehicle in a rectangular coordinate system at the current positioning time; perform precise point positioning GNSS-PPP using the constraint condition in combination with observation data of the vehicle-mounted GNSS receiver to obtain the second positioning result; control the vehicle using the second positioning result.
 11. The apparatus according to claim 10, wherein the at least one processor is further enabled to: determine a position of the GNSS receiver in the rectangular coordinate system according to the first positioning result; constructing the constraint condition according to the position of the GNSS receiver in the rectangular coordinate system, wherein the constraint condition satisfies the following equations: (X_(Lidar_fix) − X)² + (Y_(Lidar_fix) − Y)² + (Z_(Lidar_fix) − Z)² = 0 $\frac{\partial X_{{Lidar}\_{fix}}}{\partial X} = 1$ $\frac{\partial Y_{{Lidar}\_{fix}}}{\partial Y} = 1$ ${\frac{\partial Z_{{Lidar}\_{fix}}}{\partial Z} = 1};$ wherein X_(Lidar_fix) represents x-coordinate of the vehicle-mounted GNSS receiver in the rectangular coordinate system, Y_(Lidar_fix) represents y-coordinate of the vehicle-mounted GNSS receiver in the rectangular coordinate system, Z_(Lidar_fix) represents z-coordinate of the vehicle-mounted GNSS receiver in the rectangular coordinate system, X represents x-coordinate of the second positioning result to be solved, Y represents y-coordinate of the second positioning result to be solved, and Z represents z-coordinate of the second positioning result to be solved.
 12. The apparatus according to claim 10, wherein the at least one processor is further enabled to: construct observation equations using the observation data; perform PPP positioning using the observation equations in combination with the constraint condition to obtain the second positioning result, wherein the observation equations are as follows: λφ=r+l·dx+dt·C+T+I+ε+λ·N ρ=r+l·dx+dt·C+T−I+ε; wherein ρ represents a pseudo-range between a satellite and the vehicle, N represents a carrier phase integer ambiguity, r represents a distance between the satellite and the GNSS receiver, l represents cosine of an observation direction, dt represents a correction amount of a clock difference between the satellite and the vehicle-mounted GNSS receiver, T represents a tropospheric deviation, I represents an ionospheric deviation, ε represents a noise constant, dx represents a state variable to be estimated, comprising a coordinate increment and a clock difference change, and C represents a speed of light in a vacuum.
 13. The apparatus according to claim 10, wherein the at least one processor is further enabled to: convert the point cloud data from a vehicle body coordinate system to a world coordinate system to obtain converted data; project the converted data to the laser point cloud reflection value map to obtain a projection area; determine a plurality of to-be-matched areas from the laser point cloud reflection value map according to the projection area; determine a matching probability of the projection area and each of to-be-matched areas to obtain a plurality of matching probabilities; determine the first positioning result according to the plurality of matching probabilities.
 14. The apparatus according to claim 13, wherein the at least one processor is further enabled to: when determining the first positioning result according to the plurality of matching probabilities, determine a prediction probability of each of the plurality of matching probabilities to obtain a plurality of prediction probabilities, wherein each of the prediction probabilities is a matching probability corresponding to a first positioning time before the current positioning time; update each matching probability of the plurality of matching probabilities using respective prediction probabilities corresponding to the plurality of matching probabilities to obtain a plurality of updated matching probabilities; determine a maximum matching probability from the plurality of updated matching probabilities; determine the first positioning result using a to-be-matched area corresponding to the maximum matching probability.
 15. The apparatus according to claim 14, wherein the at least one processor is further enabled to: when updating each matching probability of the plurality of matching probabilities using respective prediction probabilities corresponding to the plurality of matching probabilities to obtain the plurality of updated matching probabilities, for each matching probability, determine an updated matching probability using a product of a preset normalization coefficient, the matching probability and the prediction probability corresponding to the matching probability.
 16. The apparatus according to claim 10, wherein the at least one processor is further enabled to: construct pseudo-range and carrier single difference observation equations before the at least one processor performs the precise point positioning GNSS-PPP using the constraint condition in combination with the observation data of the vehicle-mounted GNSS receiver to obtain the second positioning result; filter the observation data using the pseudo-range and carrier single difference observation equations to filter out error data in the observation data.
 17. The apparatus according to claim 10, wherein the at least one processor is further enabled to: before the at least one processor determining the first positioning result using the point cloud data of the vehicle at the current positioning time, divide a ground plane of an earth surface in a world coordinate system into a plurality of map_nodes with a same size and shape, divide each map_node of the plurality of map_nodes into a plurality of map_cells with a same size and shape, and store corresponding map data in each of the plurality of map_cells.
 18. The apparatus according to claim 17, wherein the map data comprises at least one of the following data: a mean value of laser reflection intensity values of laser points within a positioning position corresponding to the map_cell, a variance of the laser reflection intensity values of the laser points within the positioning position corresponding to the map_cell, and a quantity of the laser points within the positioning position corresponding to the map_cell.
 19. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used for causing a computer to execute the method according to claim
 1. 20. A positioning method, comprising: obtaining point cloud data using lidar on a vehicle; determining a first positioning result using the point cloud data, wherein the first positioning result is used to indicate a position of the vehicle in a pre-constructed laser point cloud reflection value map at a current positioning time; constructing a constraint condition using the first positioning result so as to accelerate a convergence speed of solving a receiver position using observation data collected by a global navigation satellite system Global Navigation Satellite System (GNSS) receiver on the vehicle, to obtain a second positioning result, wherein the second positioning result is used to indicate a position of the vehicle in a rectangular coordinate system at the current positioning time. 